Affective model device and method of deciding behavior of the affective model device

ABSTRACT

An affective model device and a method of deciding the behavior of an affective model device are provided. The affective model device has affective components representing trait, attitude, mood, emotion, and the like. The affective model device updates the emotion at regular time intervals or when a stimulus is received, and decides the behavior based on the updated emotion. The emotion may be updated depending on trait, attitude, and mood.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application is a continuation of U.S. patent applicationSer. No. 12/960,663 filed on Dec. 6, 2010, which claims the benefitunder 35 USC §119(a) of Korean Patent Application No. 10-2010-0006114,filed on Jan. 22, 2010, the entire disclosures of which are incorporatedherein by reference for all purposes.

BACKGROUND

1. Field

The following description relates to components that may be applied toan affective device such as a robot, a virtual character, and the like.

2. Description of the Related Art

Recently there has been an increasing interest in robots that canimitate human behavior. Such robots may be described as “affectiverobots” that are developed to exhibit specific emotions in response toexternal stimuli and make specific expressions or motions related to theemotions. To achieve this function, generally, an affective robot has apredetermined affective model.

The affective robot may perform a specific behavior in response to astimulus based on the state of its affective model. In other words, anaffective model may be used to reflect the emotional state of thecorresponding affective robot. For example, as humans have differentreactions in response to the same stimulus depending on their emotionalstate, an affective robot may also exhibit different reactions inresponse to the same stimulus according to the state of its affectivemodel.

Such an affective model may be composed of various affective components.The behaviors of an affective robot may be based on an affective modelthat includes a predetermined combination of affective components.

SUMMARY

In one general aspect, there is provided an affective model device,including: an emotion information storage configured to store: a firstaffective component that is based on input specificity and a variationinterval, a second affective component including a relatively higherinput specificity than the first affective component, a third affectivecomponent including a relatively smaller variation interval than thefirst affective component, and a fourth affective component including arelatively smaller variation interval than the second affectivecomponent and a relatively higher input specificity than the thirdaffective component, and a behavior deciding unit configured to decide abehavior of the affective model device based on at least one of: thefirst affective component, the second affective component, the thirdaffective component, and the fourth affective component.

The affective model device may further include that: the first affectivecomponent corresponds to a trait of the affective model device, thesecond affective component corresponds to an attitude of the affectivemodel device, the third affective component corresponds to a mood of theaffective model device, and the fourth affective component correspondsto an emotion of the affective model device.

The affective model device may further include: an affective informationmanager configured to update the fourth affective component using atleast one of: the first affective component, the second affectivecomponent, and the third affective component, and an affectiveinformation communication unit configured to provide the updated fourthaffective component to the behavior deciding unit.

The affective model device may further include that the affectiveinformation manager and the affective information communication unitinclude independent processing modules, independent processes, orindependent threads.

The affective model device may further include that the affectiveinformation communication unit is further configured to: transfer theupdated fourth affective component to the behavior deciding unit, andreceive user feedback in response to a behavior decided on by thebehavior deciding unit.

The affective model device may further include that the affectiveinformation manager is further configured to update at least one of: thefirst affective component, the second affective component, the thirdaffective component, and the fourth affective component, based on thereceived user feedback.

The affective model device may further include a stimulus interpreterconfigured to convert a received stimulus or a received input into apredetermined format in order for the affective information manager toprocess the received stimulus or the received input.

The affective model device may further include that, in response toreceiving two or more kinds of stimuli or inputs, the stimulusinterpreter is further configured to use: a weighted average strategyinvolving adding different weights to the respective stimuli, or awinner-takes-all (WTA) strategy involving selecting only one of the twoor more kinds of stimuli or inputs.

In another general aspect, there is provided a method for deciding thebehavior of an affective model device, the method including: storing afirst affective component that is based on input specificity and avariation interval, storing a second affective component including arelatively higher input specificity than the first affective component,storing a third affective component including a relatively smallervariation interval than the first affective component, storing a fourthaffective component including a relatively smaller variation intervalthan the second affective component and a relatively higher inputspecificity than the third affective component, and deciding a behaviorof the affective model device based on at least one of: the firstaffective component, the second affective component, the third affectivecomponent, and the fourth affective component.

The method may further include that: the first affective componentcorresponds to a trait of the affective model device, the secondaffective component corresponds to an attitude of the affective modeldevice, the third affective component corresponds to a mood of theaffective model device, and the fourth affective component correspondsto an emotion of the affective model device.

The method may further include updating the fourth affective componentusing at least one of: the first affective component, the secondaffective component, and the third affective component.

The method may further include that the deciding of the behaviorincludes deciding the behavior based on the updated fourth affectivecomponent.

The method may further include converting a received stimulus or areceived input into a predetermined format according to a predeterminedstrategy.

The method may further include that in response to two or more kinds ofstimuli or inputs being received, the strategy includes: a weightedaverage strategy of adding different weights to the respective stimulior inputs, or a winner-takes-all (WTA) strategy of selecting only one ofthe two or more kinds of stimuli or inputs.

The method may further include: receiving user feedback in response to abehavior of the affective model device, and updating at least one of:the first affective component, the second affective component, the thirdaffective component, and the fourth affective component, based on thereceived user feedback.

In another general aspect, there is provided a computer-readable storagemedium storing a method for deciding the behavior of an affective modeldevice, including: storing a first affective component that is based oninput specificity and a variation interval, storing a second affectivecomponent including a relatively higher input specificity than the firstaffective component, storing a third affective component including arelatively smaller variation interval than the first affectivecomponent, storing a fourth affective component including a relativelysmaller variation interval than the second affective component and arelatively higher input specificity than the third affective component,and deciding a behavior of the affective model device based on at leastone of: the first affective component, the second affective component,the third affective component, and the fourth affective component.

Other features and aspects may be apparent from the followingdescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of an affective modelsystem.

FIG. 2 is a diagram illustrating an example of an affective modeldevice.

FIG. 3 is a diagram illustrating another example of an affective modeldevice.

FIG. 4 is a diagram illustrating an example of the relationship betweenaffective components.

FIG. 5 is a diagram illustrating an example of trait parameters.

FIG. 6 is a diagram illustrating an example of attitude parameters.

FIG. 7 is a diagram illustrating an example of mood parameters.

FIG. 8 is a diagram illustrating an example of emotion parameters.

FIG. 9 is a flowchart illustrating an example of a method for decidingthe behavior of an affective model device.

Throughout the drawings and the description, unless otherwise described,the same drawing reference numerals should be understood to refer to thesame elements, features, and structures. The relative size and depictionof these elements may be exaggerated for clarity, illustration, andconvenience.

DETAILED DESCRIPTION

The following description is provided to assist the reader in gaining acomprehensive understanding of the methods, apparatuses, and/or systemsdescribed herein. Accordingly, various changes, modifications, andequivalents of the methods, apparatuses, and/or systems described hereinmay be suggested to those of ordinary skill in the art. The progressionof processing steps and/or operations described is an example; however,the sequence of steps and/or operations is not limited to that set forthherein and may be changed as is known in the art, with the exception ofsteps and/or operations necessarily occurring in a certain order. Also,descriptions of well-known functions and constructions may be omittedfor increased clarity and conciseness.

FIG. 1 illustrates an example of an affective model system.

Referring to FIG. 1, affective model system 101 receives various stimuliand performs a behavior or motion in response to the various stimuli.The affective model system 101 may be an affective model device havingpredetermined affective components 102, or a user device, for example arobot, or terminal such as a mobile phone, a PDA, and the like, in whichthe affective model device may be installed. In the current example, theaffective model system 101 refers to both an affective model device anda user device in which the affective model device is installed.

Various kinds of stimuli may be input to the affective model system 101.For example, if the affective model system 101 is a robot, the robot mayreceive various sensed data as its inputs. The sensed data may include,for example, image data acquired by means of a camera, acceleration dataacquired by an accelerometer, sound data acquired by a microphone, timeand location data acquired by a GPS system, and the like.

The behavior or motion of the affective model system 101 that is to beperformed based on the received stimulus depends on the affectivecomponents 102 stored in the affective model system 101. The affectivecomponents 102 may include personality traits, attitudes, moods,emotions, and the like. For example, when playing a trick on a person,the person's reaction may be different between based on whether theperson is angry or if the person is in good spirits. Accordingly, theaffective model system 101 may also exhibit different behavior patternsin response to the same stimulus depending on its affective components102.

FIG. 2 illustrates an example of an affective model device.

Referring to FIG. 2, affective model device 200 includes an affectiveinformation storage 201, an affective information manager 202, anaffective information communication unit 203, and a behavior decidingunit 204.

The affective information storage 201 may store at least one affectivecomponent. Affective components may be represented on a 2-dimensionalaffective plane in which one axis corresponds to input specificityagainst an external stimulus or input and in which the other axiscorresponds to a variation interval or life-time. For example, theaffective components may include a first affective component thatoccupies a portion on the affective plane, a second affective componentthat has a relatively higher input specificity than the first affectivecomponent, a third affective component that has a relatively smallervariation interval than the first affective component, and a fourthaffective component that has a relatively smaller variation intervalthan the second affective component and relatively higher inputspecificity than the third affective component.

The first affective component may be represented as a trait parameterindicating a trait of the affective model device 200. Because it may behard to change a person's personality, the first affective component ortrait parameters may be maintained initially for an extended duration oftime.

The second affective component may be represented as an attitudeparameter indicating an attitude of the affective model device 200. Theattitude parameter may be a degree of likability of the affective modeldevice 200 with respect to certain persons or items. For example, if theaffective model device 200 is installed in a robot, an attitudeparameter of the robot 200 may have a positive value with respect to itsowner.

The third affective component may be represented as a mood parameterindicating a mood of the affective model device 200. For example, if theaffective model device 200 charges a battery of the robot 200 at aspecific time, a mood parameter at that time may be illustrated as apositive value.

The fourth affective component may be represented as an emotionparameter indicating an emotion of the affective model device 200. Whenthe affective model device 200 is installed in a robot and its ownerpats the robot 200, a value of an emotion component corresponding to“Joy” may be increased.

The affective information manager 202 may update affective informationstored in the affective information storage 201. The time interval atwhich affective information is to be updated may be arbitrarily set. Forexample, affective information may be updated at regular time intervalsor the affective information may be updated in response to an externalstimulus. Accordingly, the affective information manager 202 may updateemotion parameters based on trait, attitude, and/or mood parametersstored in the affective information storage 201. For example, inresponse to receiving a stimulus, the affective information manager 202may select an emotion parameter that is to be updated, and may increaseor decrease an intensity of the selected emotion parameter based ontrait, attitude, and/or mood parameters.

The affective information communication unit 203 may provide affectivecomponents stored in the affective information storage 201 or affectivecomponents updated by the affective information manager 202 to thebehavior deciding unit 204. For example, the affective informationcommunication unit 203 may transfer the updated emotion parameter to thebehavior deciding unit 204.

Based on the received affective component, the behavior deciding unit204 decides a behavior to be exhibited by the affective model device200.

The affective information communication unit 203 may receive a user'sfeedback or user's response to a behavior decided by the behaviordeciding unit 204 and transfer the feedback or response to the affectiveinformation manager 202. The affective information manager 202 mayupdate the affective components stored in the affective informationstorage 201 based on the received user's feedback or response.

The affective information storage 201, the affective information manager202, the affective information communication unit 203, and the behaviordeciding unit 204 may be independent processing modules, processors, orthreads. For example, any one of the affective information storage 201,the affective information manager 202, the affective informationcommunication unit 203, and the behavior deciding unit 204 may comprisevarious configurations, and the remaining modules may not be influencedby the variations. Accordingly, the respective components 201, 202, 203,and 204 may be independently designed modules.

Because the affective model device 200 is configured based on affectivecomponents, such as traits, attitudes, moods, emotions and the like, theaffective model device 200 may exhibit various reactions depending onsituations, similar to people. In addition, because the respectiveelements are configured as independent modules, reusability andindependency may be ensured.

FIG. 3 illustrates another example of an affective model device.

Referring to FIG. 3, affective model device 300 includes an affectiveinformation storage 201, an affective information manager 202, anaffective information communication unit 203, a behavior deciding unit204, a stimulus interpreter 301, and a configuration file storage 302.In the affective model device 300, the affective information storage201, the affective information manager 202, the affective informationcommunication unit 203, and the behavior deciding unit 204 correspond toelements described with reference to FIG. 2.

The stimulus interpreter 301 may convert a received stimulus into aformat that may be used by the affective information manager 202. Forexample, when the affective model device 300 is applied to user devicessuch as a robot, or a terminal such as a mobile phone, a PDA, and thelike, the user devices may receive and use various kinds of inputs. Assuch, the affective model device 300 may be installed in variousdifferent user devices, and the stimulus interpreter 301 may preprocessa received stimulus to be converted into a predetermined format,regardless of the type of a user device in which the affective modeldevice 300 is installed. Accordingly, the affective information manager202 may process the stimulus based on various inputs.

The configuration file storage 302 may store a strategy forpreprocessing stimuli. For example, the stimulus interpreter 301 maypreprocess a received stimulus according to a predetermined strategythat is stored in the configuration file storage 302. A first example ofthe strategy is weighted average. In response to receiving two or morekinds of stimuli, the weighted average method may add the respectivestimuli using the same weight or different weights. Weight values may beassigned to stimuli and may be based on the use purpose or applicationtype. A second example of the strategy is winner-takes-all (WTA) method.In response to receiving two or more kinds of stimuli, thewinner-takes-all (WTA) method may accept only one of the receivedstimuli and ignore the other remaining stimuli.

The configuration file storage 302 may store default values (or basicvalues) of the affective parameters that are stored in the affectiveinformation storage 201. When updating affective parameters stored inthe affective information storage 201 or when storing and settinginitial affective parameters in the affective information storage 201,the affective information manager 202 may refer to the default values ofthe affective parameters stored in the configuration file storage 302.

FIG. 4 illustrates an example of the relationship between affectivecomponents.

Referring to the example shown in FIG. 4, the affective components mayinclude a trait component 401, an attitude component 402, a moodcomponent 403, and an emotion component 404. The affective components401, 402, 403 and 404 may be represented on an affective plane based oninput specificity and variation interval. The X-axis of the affectiveplane corresponds to a variation interval. For example, when certainaffective components have a positive (+) value in an X-axis direction,the affective component may be determined to have a relatively longvariation interval. Also, when affective components have a negative (−)value in an X-axis direction, the affective component may have arelatively short variation interval.

The Y-axis of the affective plane corresponds to input specificity. Forexample, as certain affective components have a negative (−) value in aY-axis direction, the affective component is determined to react morespecifically to an external stimulus or input, whereas components have apositive (+) value in a Y-axis directions may be determined to reactless specifically to an external stimulus or input.

Referring to the example shown in FIG. 4, the trait component 401 may bea component that has little influence from time and little inputspecificity and is a basic affective component which influencesdifferent affective components. For example, the trait component 401 maybe composed of trait components, such as openness, conscientiousness,extraversion, agreeableness, neuroticism, and the like.

The emotion component 404 may be an affective component exhibitingimmediate variations in response to inputs while having specificityagainst predetermined inputs. For example, the emotion component 404 maybe composed of emotional components, such as joy, interest, surprise,fear, anger, sadness, disgust, and the like. The emotion component 404may be influenced by the trait component 401, attitude component 402,and mood component 403.

In one example, the mood component 403 is an affective component whichis less sensitive to time in comparison to the emotion component 404while having little specificity. The mood component 403 may varydepending on time or place.

The attitude component 402 is an affective component having littlevariations with respect to time while showing specificity to humans oritems.

FIG. 5 illustrates an example of trait parameters. Each trait parametermay correspond to a first affective component.

Referring to FIG. 5, the trait parameters may be composed of a pluralityof trait components and trait values corresponding to the traitcomponents.

For example, the trait components may include values for openness,conscientiousness, extraversion, agreeableness, and neuroticism. Thetrait values may indicate mean intensities for the respective traitcomponents. For example, as illustrated in FIG. 5, an affective modelsystem (e.g., the affective model system 101 of FIG. 1) that has anopenness of 100 may exhibit relatively more inquisitive, imaginativebehaviors than an affective model system having an openness of 50.

FIG. 6 illustrates an example of attitude parameters. Each attitudeparameter may correspond to a second affective component.

Referring to FIG. 6, the attitude parameters may be composed ofpredetermined objects and attitude values about the objects.

For example, the predetermined objects may be persons or items thatreact with the affective model system. The attitude values may be meandegrees of likability about the corresponding objects. The attitudevalues may be positive or negative values depending on positive ornegative likability. For example, when the affective model system is arobot, the robot 101 may have a mean attitude value of +73 for its ownerA and a mean attitude value of 0 for a stranger C.

FIG. 7 illustrates an example of mood parameters. Each mood parametermay correspond to a third affective component.

Referring to FIG. 7, the mood parameters may be composed of time valuesand mood values. For example, mood parameters may be composed ofpositive mood values indicating a good mood and negative mood valuesindicating a bad mood for various time intervals. For example, the moodparameters may vary over time like the human biorhythm. In the currentexample, the criteria for varying mood values is time, however, this ismerely for purposes of example. For example, the mood values may varydepending on place, time of year, weather, and the like.

FIG. 8 illustrates an example of emotion parameters. Each emotionparameter may correspond to a fourth affective component.

Referring to FIG. 8, the emotion parameter may be composed of aplurality of emotion components and emotion values for the emotioncomponents.

For example, the emotion components may include joy, interest, surprise,fear, anger, sadness, disgust, and the like. The emotion values mayindicate mean intensities of the individual emotion components.

The emotion value for a certain emotion component may be updated atregular time intervals or in response to a certain stimulus beingreceived. The update degree of the emotion value, that is, theincrease/decrease degree of the emotion value may depend on variousaffective parameters as described with reference to FIGS. 5 to 7, suchas, the trait, attitude and mood parameters. For example, in response toa stimulus being applied, a mood parameter may vary depending on whenthe stimulus is applied and an update degree of an emotion parameter mayvary depending on the mood parameter.

FIG. 9 is a flowchart illustrating an example of a method for decidingthe behavior of an affective model device.

Referring to FIG. 9, the affective information may be stored inoperation 1001. The affective information may be trait, attitude, moodand emotion parameters described above with reference to FIGS. 4 to 8.For example, referring again to FIGS. 2 and 3, the affective informationmanager 202 may load default values for the respective affectiveparameters from the configuration file storage 302 and store the defaultvalues in the affective information storage 201.

In operation 1002, the emotion may be updated. For example, the emotioninformation manager 202 may update emotion parameters among affectiveparameters stored in the emotion information storage 201. Updatingemotion parameters may be done at regular time intervals or in responseto an external stimulus being received.

The updated emotion may be provided in operation 1003. For example, theaffective information communication unit 203 may transfer the updatedemotion parameter to the behavior deciding unit 204.

In operation 1004, a behavior may be decided based on the providedemotion. For example, the behavior deciding unit 204 may decide abehavior to be exhibited by the affective model system 101 based on thereceived emotion parameter.

A response to the behavior may be received in operation 1005. Forexample, the behavior deciding unit 204 may receive user feedback inresponse to the behavior and transfer the received user feedback to theaffective information communication unit 203.

In operation 1006, the affective information may be again updated. Forexample, the affective information manager 202 may receive user feedbackfrom the affective information communication unit 203 and update any oneof attitude, mood and emotion parameters.

As a non-exhaustive illustration only, the terminal device describedherein may refer to mobile devices such as a cellular phone, a personaldigital assistant (PDA), a digital camera, a portable game console, anMP3 player, a portable/personal multimedia player (PMP), a handhelde-book, a portable lab-top personal computer (PC), a global positioningsystem (GPS) navigation, and devices such as a desktop PC, a highdefinition television (HDTV), an optical disc player, a setup box, andthe like, capable of wireless communication or network communicationconsistent with that disclosed herein.

A computing system or a computer may include a microprocessor that iselectrically connected with a bus, a user interface, and a memorycontroller. It may further include a flash memory device. The flashmemory device may store N-bit data via the memory controller. The N-bitdata is processed or will be processed by the microprocessor and N maybe 1 or an integer greater than 1. Where the computing system orcomputer is a mobile apparatus, a battery may be additionally providedto supply operation voltage of the computing system or computer.

It should be apparent to those of ordinary skill in the art that thecomputing system or computer may further include an application chipset,a camera image processor (CIS), a mobile Dynamic Random Access Memory(DRAM), and the like. The memory controller and the flash memory devicemay constitute a solid state drive/disk (SSD) that uses a non-volatilememory to store data.

The processes, functions, methods and/or software described above may berecorded, stored, or fixed in one or more computer-readable storagemedia that includes program instructions to be implemented by a computerto cause a processor to execute or perform the program instructions. Themedia may also include, alone or in combination with the programinstructions, data files, data structures, and the like. The media andprogram instructions may be those specially designed and constructed, orthey may be of the kind well-known and available to those having skillin the computer software arts. Examples of computer-readable mediainclude magnetic media, such as hard disks, floppy disks, and magnetictape; optical media such as CD-ROM disks and DVDs; magneto-opticalmedia, such as optical disks; and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory (ROM), random access memory (RAM), flash memory, and the like.Examples of program instructions include machine code, such as producedby a compiler, and files containing higher level code that may beexecuted by the computer using an interpreter. The described hardwaredevices may be configured to act as one or more software modules inorder to perform the operations and methods described above, or viceversa. In addition, a computer-readable storage medium may bedistributed among computer systems connected through a network andcomputer-readable codes or program instructions may be stored andexecuted in a decentralized manner.

A number of examples have been described above. Nevertheless, it shouldbe understood that various modifications may be made. For example,suitable results may be achieved if the described techniques areperformed in a different order and/or if components in a describedsystem, architecture, device, or circuit are combined in a differentmanner and/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe following claims.

What is claimed is:
 1. An affective model device, comprising: an emotion information storage configured to store at least two of: a first affective component that is based on input specificity and a variation interval; a second affective component comprising a relatively higher input specificity than the first affective component; a third affective component comprising a relatively smaller variation interval than the first affective component; and a fourth affective component comprising a relatively smaller variation interval than the second affective component and a relatively higher input specificity than the third affective component; and a behavior deciding unit configured to decide a behavior of the affective model device based on at least one of: the first affective component, the second affective component, the third affective component, and the fourth affective component.
 2. The affective model device of claim 1, wherein: the first affective component corresponds to a trait of the affective model device; the second affective component corresponds to an attitude of the affective model device; the third affective component corresponds to a mood of the affective model device; and the fourth affective component corresponds to an emotion of the affective model device.
 3. The affective model device of claim 1, further comprising: an affective information manager configured to update the fourth affective component using at least one of: the first affective component, the second affective component, and the third affective component; and an affective information communication unit configured to provide the updated fourth affective component to the behavior deciding unit.
 4. The affective model device of claim 3, wherein the affective information manager and the affective information communication unit comprise independent processing modules, independent processes, or independent threads.
 5. The affective model device of claim 3, wherein the affective information communication unit is further configured to: transfer the updated fourth affective component to the behavior deciding unit; and receive user feedback in response to a behavior decided on by the behavior deciding unit.
 6. The affective model device of claim 5, wherein the affective information manager is further configured to update at least one of: the first affective component, the second affective component, the third affective component, and the fourth affective component, based on the received user feedback.
 7. The affective model device of claim 3, further comprising a stimulus interpreter configured to convert a received stimulus or a received input into a predetermined format in order for the affective information manager to process the received stimulus or the received input.
 8. The affective model device of claim 7, wherein, in response to receiving two or more kinds of stimuli or inputs, the stimulus interpreter is further configured to use: a weighted average strategy involving adding different weights to the respective stimuli; or a winner-takes-all (WTA) strategy involving selecting only one of the two or more kinds of stimuli or inputs.
 9. A method for deciding the behavior of an affective model device, the method comprising: storing at least two of: a first affective component that is based on input specificity and a variation interval, a second affective component comprising a relatively higher input specificity than the first affective component, a third affective component comprising a relatively smaller variation interval than the first affective component, and a fourth affective component comprising a relatively smaller variation interval than the second affective component and a relatively higher input specificity than the third affective component; and deciding a behavior of the affective model device based on at least one of: the first affective component, the second affective component, the third affective component, and the fourth affective component.
 10. The method of claim 9, wherein: the first affective component corresponds to a trait of the affective model device; the second affective component corresponds to an attitude of the affective model device; the third affective component corresponds to a mood of the affective model device; and the fourth affective component corresponds to an emotion of the affective model device.
 11. The method of claim 9, further comprising updating the fourth affective component using at least one of: the first affective component, the second affective component, and the third affective component.
 12. The method of claim 11, wherein the deciding of the behavior comprises deciding the behavior based on the updated fourth affective component.
 13. The method of claim 9, further comprising converting a received stimulus or a received input into a predetermined format according to a predetermined strategy.
 14. The method of claim 13, wherein, response to two or more kinds of stimuli or inputs being received, the strategy comprises: a weighted average strategy of adding different weights to the respective stimuli or inputs; or a winner-takes-all (WTA) strategy of selecting only one of the two or more kinds of stimuli or inputs.
 15. The method of claim 9, further comprising: receiving user feedback in response to a behavior of the affective model device; and updating at least one of: the first affective component, the second affective component, the third affective component, and the fourth affective component, based on the received user feedback.
 16. A computer-readable storage medium storing a program for executing a method for deciding the behavior of an affective model device, the method comprising: storing at least two of: a first affective component that is based on input specificity and a variation interval, a second affective component comprising a relatively higher input specificity than the first affective component, a third affective component comprising a relatively smaller variation interval than the first affective component, and a fourth affective component comprising a relatively smaller variation interval than the second affective component and a relatively higher input specificity than the third affective component; and deciding a behavior of the affective model device based on at least one of: the first affective component, the second affective component, the third affective component, and the fourth affective component. 